Peer-to-Peer(P2P) systems have emerged as a promising paradigm to structure large scale distributed systems. They provide a robust, scalable and decentralized way to share and publish data.The unstructured P2P systems have gained much popularity in recent years for their wide applicability and simplicity. However efficient resource discovery remains a fundamental challenge for unstructured P2P networks due to the lack of a network structure. To effectively harness the power of unstructured P2P systems, the challenges in distributed knowledge management and information search need to be overcome. Current attempts to solve the problems pertaining to knowledge management and search have focused on simple term based routing indices and keyword search queries. Many P2P resource discovery applications will require more complex query functionality, as users will publish semantically rich data and need efficiently content location algorithms that find target content at moderate cost. Therefore, effective knowledge and data management techniques and search tools for information retrieval are imperative and lasting. In my dissertation, I present a suite of protocols that assist in efficient content location and knowledge management in unstructured Peer-to-Peer overlays. The basis of these schemes is their ability to learn from past peer interactions and increasing their performance with time.My work aims to provide effective and bandwidth-efficient searching and data sharing in unstructured P2P environments. A suite of algorithms which provide peers in unstructured P2P overlays with the state necessary in order to efficiently locate, disseminate and replicate objects is presented. Also, Existing approaches to federated search are adapted and new methods are developed for semantic knowledge representation, resource selection, and knowledge evolution for efficient search in dynamic and distributed P2P network environments. Furthermore,autonomous and decentralized algorithms that reorganizes an unstructured network topology into a one with desired search-enhancing properties are proposed in a network evolution model to facilitate effective and efficient semantic search in dynamic environments.

This research contributes to the field of ontology-based semantic matching techniques and also to the field of Instant Messaging (IM) based enhanced presence. It aims to achieve a mutually beneficial development of two fields through interactions in their use of data and their functionality. With respect to semantic matching this research has developed a collaborative and self-evolutionary approach based on user involvement in order to overcome disadvantages of traditional ontology-based approaches. At the same time, enhanced semantic matching algorithms were also explored and developed to achieve better performance when searching and querying through the ontology. In order to realize this automatic, dynamic and collaborative approach, a Jabber-based IM system was built to support its development with specific data and to evaluate its performance. In the prototype of the system, Computer Science area is selected to be the domain of the ontology in order to demonstrate the practicability of the new approach. With respect to enhanced presence an efficient semantic-based contacts search engine which can feature context-based search ranking is provided to support academic researchers. It is especially designed to help new academic researchers to find potential contacts who share a common research interest. It enriches the IM system's presence information, and helps the user to pick the most suitable contacts and conveniently organize meetings or co-operating with others. Consequently, this research improves the efficiency of users' academic researching, and extends users' relationship radius during their academic research careers. The contributions are particularly highlighted by the comprehensive support during the academic user's self-educational process.

The rapid development of mobile technologies has facilitated users to generate and store files on mobile devices. However, it has become a challenging issue for users to search efficiently and effectively for files of interest in a mobile environment that involves a large number of mobile nodes. In this thesis, file management and retrieval alternatives have been investigated to propose a feasible framework that can be employed on resource-limited devices without altering their operating systems. The file annotation and retrieval framework (FARM) proposed in the thesis automatically annotates the files with their basic file attributes by extracting them from the underlying operating system of the device. The framework is implemented in the JME platform as a case study. This framework provides a variety of features for managing the metadata and file search features on the device itself and on other devices in a networked environment. FARM not only automates the file-search process but also provides accurate results as demonstrated by the experimental analysis. In order to facilitate a file search and take advantage of the Semantic Web Technologies, the SemFARM framework is proposed which utilizes the knowledge of a generic ontology. The generic ontology defines the most common keywords that can be used as the metadata of stored files. This provides semantic-based file search capabilities on low-end devices where the search keywords are enriched with additional knowledge extracted from the defined ontology. The existing frameworks annotate image files only, while SemFARM can be used to annotate all types of files. Semantic heterogeneity is a challenging issue and necessitates extensive research to accomplish the aim of a semantic web. For this reason, significant research efforts have been made in recent years by proposing an enormous number of ontology alignment systems to deal with ontology heterogeneities. In the process of aligning different ontologies, it is essential to encompass their semantic, structural or any system-specific measures in mapping decisions to produce more accurate alignments. The proposed solution, in this thesis, for ontology alignment presents a structural matcher, which computes the similarity between the super-classes, sub-classes and properties of two entities from different ontologies that require aligning. The proposed alignment system (OARS) uses Rough Sets to aggregate the results obtained from various matchers in order to deal with uncertainties during the mapping process of entities. The OARS uses a combinational approach by using a string-based and linguistic-based matcher, in addition to structural-matcher for computing the overall similarity between two entities. The performance of the OARS is evaluated in comparison with existing state of the art alignment systems in terms of precision and recall. The performance tests are performed by using benchmark ontologies and the results show significant improvements, specifically in terms of recall on all groups of test ontologies. There is no such existing framework, which can use alignments for file search on mobile devices. The ontology alignment paradigm is integrated in the SemFARM to further enhance the file search features of the framework as it utilises the knowledge of more than one ontology in order to perform a search query. The experimental evaluations show that it performs better in terms of precision and recall where more than one ontology is available when searching for a required file.

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A diversidade biológica é essencial para a sustentabilidade da vida na Terra e motiva numerosos esforços para coleta de dados sobre espécies, dando origem a uma grande quantidade de informação. Esses dados são geralmente armazenados em bancos de dados relacionais. Pesquisadores usam esses bancos de dados para extrair conhecimento e compartilhar novas descobertas. No entanto, atualmente a busca tradicional (baseada em palavras-chave) já não é adequada para ser usada em grandes quantidades de dados heterogêneos, como os de biodiversidade. Ela tem baixa precisão e revocação para esse tipo de dado. Este trabalho apresenta uma nova arquitetura para abordar esse problema aplicando técnicas de buscas semânticas em dados sobre biodiversidade e usando formatos e ferramentas da Web Semântica para representar esses dados. A busca semântica tem como objetivo melhorar a acurácia dos resultados de buscas com o uso de ontologias para entender os objetivos dos usuários e o significado contextual dos termos utilizados. Este trabalho também apresenta os resultados de testes usando um conjunto de dados representativos sobre biodiversidade do Instituto Nacional de Pesquisas da Amazônia (INPA) e do Museu Paraense Emílio Goeldi (MPEG). Ontologias permitem que conhecimento seja organizado em espaços conceituais de acordo com seu significado. Para a busca semântica funcionar, um ponto chave é a criação de mapeamentos entre os dados (neste caso, dados sobre biodiversidade do INPA e MPEG) e termos das ontologias que os descrevem, neste caso: a classificação taxonômica de espécies e a OntoBio, a ontologia de biodiversidade do INPA. Esses mapeamentos foram criados depois que extraímos a classificação taxonômica do site Catalog of Life (CoL) e criamos uma nova versão da OntoBio. Um protótipo da arquitetura foi construído e testado usando casos de uso e dados do INPA e MPEG. Os resultados dos testes mostraram que a abordagem da busca semântica tinha uma melhor precisão (28% melhor) e revocação (25% melhor) quando comparada com a busca por palavras-chave. Eles também mostraram que é possível conectar facilmente os dados mapeados a outras fontes de dados abertas, como a fonte Amazon Forest Linked Data do Instituto Nacional de Pesquisas Espaciais. (INPE) / Biological diversity is of essential value to life sustainability on Earth and motivates many efforts to collect data about species. That gives rise to a large amount of information. Biodiversity data, in most cases, is stored in relational databases. Researchers use this data to extract knowledge and share their new discoveries about living things. However, nowadays the traditional search approach (based basically on keywords matching) is not appropriate to be used in large amounts of heterogeneous biodiversity data. Search by keyword has low precision and recall in this kind of data. This work presents a new architecture to tackle this problem using a semantic search system for biodiversity data and semantic web formats and tools to represent this data. Semantic search aims to improve search accuracy by using ontologies to understand user objectives and the contextual meaning of terms used in the search to generate more relevant results. This work also presents test results using a set of representative biodiversity data from the National Research Institute for the Amazon (INPA) and the Emilio Gueldi Museum in Pará (MPEG). Ontologies allow knowledge to be organized into conceptual spaces in accordance to its meaning. For semantic search to work, a key point is to create mappings between the data (in this case, INPAs and MPEGs biodiversity data) and the ontologies describing it, in this case: the species taxonomy (a taxonomy is an ontology where each class can have just one parent) and OntoBio, INPAs biodiversity ontology. These mappings were created after we extracted the taxonomic classification from the Catalogue of Life (CoL) website and created a new version of OntoBio. A prototype of the architecture was built and tested using INPAs and MPEGs use cases and data. The results showed that the semantic search approach had a better precision (28% improvement) and recall (25% improvement) when compared to keyword based search. They also showed that it was possible to easily connect the mapped data to other Linked Open Data sources, such as the Amazon Forest Linked Data from the National Institute for Space Research (INPE)

<p>Advances in peer-to-peer overlay networks and Semantic Web technology will have a substantial influence on the design and implementation of future digital libraries. However, it remains unclear how best to combine their advantages in constructing digital library systems. This thesis is devoted for investigating, proposing and evaluating possible solutions to advance developments in this field.</p><p>The main research goal of this work is to combine the strengths of both peer-to-peer overlay networks and Semantic Web for facilitating semantic searches in large-scale distributed digital library systems. The approach has been conducted in a sequential and progressive manner. Firstly, we recognize system infrastructure and metadata heterogeneity as two major challenges in conducting semantic searching across distributed digital libraries. Next, we investigate the strengths and weaknesses of both peer-to-peer and Semantic Web technology and justify that these two fields are complementary and can be combined in conducting semantic searches in a large-scale distributed environment. Thirdly, due to various topologies, functionalities and limitations different peer-to-peer infrastructures may possess, we survey current classical peer-to-peer systems so as to facilitate determinating appropriate infrastructure for specific application scenario. Fourthly, we probe into approaches in generating ontology-enriched metadata records for semantic search purpose. Finally and most importantly, we will propose a semantic search process for interoperation among heterogeneous resources, basing on ontology mapping mechanism.</p><p>A major contribution expected in our work is, in a broader term, proposing and investigating possible solutions in combining the strengths of both peer-to-peer overlay networks and Semantic Web for facilitating semantic search among highly distributed digital libraries. From a specific perspective, we provide an appropriate benchmark for facilitating decision making in choosing appropriate peer-to-peer networks for digital library construction; especially, we consider in this work no global schema exists and further justify the feasibility and advantages of ontology engineering method in semantic enriched metadata management; to support federated search in such a distributed environment, we also propose an extended super-peer network model, emphasizing in load-balancing and self-organizing capabilities; Based on semantic enriched metadata management, we propose also direct ontology mapping method to enable runtime semantic search process. Evaluation results have illustrated the feasibility and robustness of our approaches.</p><p>The future direction of this work includes studies on user authentication,efficient ontology parsing and real-life applications.</p>

Advances in peer-to-peer overlay networks and Semantic Web technology will have a substantial influence on the design and implementation of future digital libraries. However, it remains unclear how best to combine their advantages in constructing digital library systems. This thesis is devoted for investigating, proposing and evaluating possible solutions to advance developments in this field. The main research goal of this work is to combine the strengths of both peer-to-peer overlay networks and Semantic Web for facilitating semantic searches in large-scale distributed digital library systems. The approach has been conducted in a sequential and progressive manner. Firstly, we recognize system infrastructure and metadata heterogeneity as two major challenges in conducting semantic searching across distributed digital libraries. Next, we investigate the strengths and weaknesses of both peer-to-peer and Semantic Web technology and justify that these two fields are complementary and can be combined in conducting semantic searches in a large-scale distributed environment. Thirdly, due to various topologies, functionalities and limitations different peer-to-peer infrastructures may possess, we survey current classical peer-to-peer systems so as to facilitate determinating appropriate infrastructure for specific application scenario. Fourthly, we probe into approaches in generating ontology-enriched metadata records for semantic search purpose. Finally and most importantly, we will propose a semantic search process for interoperation among heterogeneous resources, basing on ontology mapping mechanism. A major contribution expected in our work is, in a broader term, proposing and investigating possible solutions in combining the strengths of both peer-to-peer overlay networks and Semantic Web for facilitating semantic search among highly distributed digital libraries. From a specific perspective, we provide an appropriate benchmark for facilitating decision making in choosing appropriate peer-to-peer networks for digital library construction; especially, we consider in this work no global schema exists and further justify the feasibility and advantages of ontology engineering method in semantic enriched metadata management; to support federated search in such a distributed environment, we also propose an extended super-peer network model, emphasizing in load-balancing and self-organizing capabilities; Based on semantic enriched metadata management, we propose also direct ontology mapping method to enable runtime semantic search process. Evaluation results have illustrated the feasibility and robustness of our approaches. The future direction of this work includes studies on user authentication,efficient ontology parsing and real-life applications.

A Content Management System (CMS) is a software application for creating, publishing, editing and managing content. The future step in content management system development is building intelligence over existing content resources that are heterogeneous in nature. Intelligence collected at the knowledge base can later on be used for executing semantic queries. Expressing the relations among content resources with ontological formalisms is therefore the key to implementing such semantic features. In this work, a methodology for the semantic lifting of JSR-170 compliant content repositories to ontologies is devised. The fact that in the worst case JSR-170 enforces no particular structural restrictions on the content model poses a technical challenge both for the initial build-up and further synchronization of the knowledge base. To address this problem, some recurring structural patterns in JSR-170 compliant content repositories are exploited. The value of the ontology extraction framework is assessed through a semantic search mechanism that is built on top of the extracted ontologies. The work in this thesis is complementary to the &ldquo / Interactive Knowledge Stack for small to medium CMS/KMS providers (IKS)&rdquo / project funded by the EC (FP7-ICT-2007-3).

Content Management Systems (CMS) generally store data in a way that the content is distributed among several relational database tables or stored in files as a whole without any distinctive characteristics. These storage mechanisms cannot provide the management of semantic information about the data. They lack semantic retrieval, search and browsing of the stored content. To enhance non-semantic CMSes with advanced semantic features, the semantics within the CMS itself and additional semantic information related with the actual managed content should also be taken into account. However, extracting implicit knowledge from the legacy CMSes, lifting to a semantic content management system environment and providing semantic operations on the content is a challenging task which includes adoption of several latest advancements in information extraction (IE), information retrieval (IR) and Semantic Web areas. In this study, we propose an integrative approach including automatic lifting of content from legacy systems, automatic annotation of data with the information retrieved from the Linked Open Data (LOD) cloud and several semantic operations on the content in terms of storage and search. We use a simple RDF path language to create custom, semantic indexes and filter annotations obtained from LOD cloud in a way that is eligible for specific use cases. Filtered annotations are materialized along with the actual content of document in dedicated indexes. This semantix indexing infrastructure allows semantically meaningful search facilities on top of it. We realize our approach in the scope of Apache Stanbol project, which is a subproject developed in the scope of IKS project, by focusing on document storage and retrival parts of it. We evaluate our approach in healthcare domain with different domain ontologies (SNOMED/CT, ART, RXNORM) in addition to DBpedia as parts of LOD cloud which are used annotate documents and content obtained from different health portals.

Semantika informacinių technologijų kontekste yra duomenų apdorojimas pagal prasmę ir kontekstą. Tam įgyvendinti yra taikomas natūralios kalbos apdorojimas, pritaikytas informacijos paieškai, išrinkimui, analizavimui.Taikant semantines technologijas, kūrėjams dažnai kyla klausimas, kaip sukurti semantinės paieškos sąsają, kad ji būtų patogi ir duotų kuo tikslesnius atsakymus į vartotojų užklausas. Šiame darbe aprašomas sukurtas metodas, kuris padeda vartotojui palaipsniui formuoti SPARQL užklausą iš atskirų elementų, dinamiškai keičiant vartotojo sąsają. / Increasing popularity of Semantic Web raises a question how we could make a simple user interface for building semantic queries while keeping high precision of results returned. This thesis presents a method that helps users to create SPARQL queries by allowing to dynamically add components to user interface. The goal of the work is to improve of user interface model for semantic queries by allowing users to construct and change it dynamically till obtaining the desirable answer results. That model was created on the base of analysis of Semantic Web languages, tools and existing portals, their functions and user interfaces. Algorithms for dynamic user interface generation based on user actions were developed that allow creating queries of various complexities with minimal amount of user interface components. Implementation and testing the prototype of the system using movie and wine ontologies has shown that dynamic construction and generation of query interface has desirable functionality and is easily applicable to various ontologies. Experimental comparison with existing semantic search portals has shown that the proposed dynamic user interface generation method could improve precision and recall of semantic queries and may be applied in semantic search portal applications.